With the abundance of unstructured data made available through various means in general and the Internet in particular, there is also a need to provide effective ways of analyzing such data. Unstructured data analysis is a challenging task, as it requires processing of big-data. Big data typically refers to a collection of data sets that are large and complex and cannot be analyzed using on-hand database management tools or traditional data processing applications.
Several prior art solutions can be used to search through big data sources. As a result of the search, relevant data elements may be extracted from such big data sources. However, a problem may occur while trying to search for additional data that may be useful, for example, data containing similar characteristics to the characteristics of the extracted data. Typically, the complexity of the data while analyzing the characteristics of big data, leads to inefficient identification of common patterns. Furthermore, the search as known today may be inefficient because of lack of correlation between data elements extracted from big data sources.
It would be therefore advantageous to provide an efficient solution to analyze big data. It would be further advantageous if such solution would enable correlating between common patterns while analyzing the big data.